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Sensors 2012, 12(12), 16988-17006; doi:10.3390/s121216988

Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions

1,* , 2
1 Instituto de Ciencias de la Vid y del Vino (CSIC, University of La Rioja, La Rioja Government) Madre de Dios, 51, 26006 Logroño, Spain 2 Department of Agricultural Engineering, ETSIA, Technical University of Madrid, Av. Complutense s/n Ciudad Universitaria, 28043 Madrid, Spain
* Author to whom correspondence should be addressed.
Received: 22 October 2012 / Revised: 5 December 2012 / Accepted: 6 December 2012 / Published: 12 December 2012
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R2 values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management.
Keywords: clustering; Mahalanobis; Vitis vinifera L.; vineyard; yield assessment clustering; Mahalanobis; Vitis vinifera L.; vineyard; yield assessment
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Diago, M.-P.; Correa, C.; Millán, B.; Barreiro, P.; Valero, C.; Tardaguila, J. Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions. Sensors 2012, 12, 16988-17006.

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